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to process tissue data reproducibly and at scale Conduct analyses using programming languages such as R and Python Collaborate with other laboratory members with expertise in epidemiology, bioinformatics
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Computer Science, Biomedical Engineering, Statistics, Biomedical Sciences, Electrical Engineering, or a related quantitative field Strong programming skills (e.g., Python, R, or similar) Demonstrated research
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, viability, cytokine readouts), and imaging/flow cytometry. Maintain rigorous records, QC, and SOP adherence for human biospecimens. Analyze/interpret data with basic scripting skills (R/Python) and
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technologies, is highly desirable. Skills: Strong knowledge of clinical informatics frameworks, standards, and methodologies. Proficiency in data analysis software (e.g., R, Python, SAS, SPSS, SQL) and
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helpful, as is comfort with multi-omics (scRNA-seq/spatial, proteomics/metabolomics) and analysis in R or Python. A translational mindset—thinking in terms of potency, QC, and GMP-relevant considerations
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field that provides a strong research background for the project. Fluency in English and Python are required. Research experience working with large-scale machine learning projects, extensive research
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publications Strong programming skills in Python and deep learning frameworks such as PyTorch, demonstrated by coursework, projects, or contributions to public code repositories Experience with or strong
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tissue specimens Assemble analysis pipelines using machine learning to process tissue data reproducibly and at scale Conduct analyses using programming languages such as R and Python Collaborate with other
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, integration, clustering, and annotation ● Proficiency in Python and/or R for large-scale data analysis ● Experience developing reproducible workflows, pipelines, and scalable data-processing frameworks
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to discover pathway‑level disease mechanisms. Build reproducible pipelines in R/Python Integrate proteomic signatures with neuroimaging, cognitive and neuropathology phenotypes. Write manuscripts, contribute